Abstract
This paper describes the development of a 3D continuous sign language recognition system. Since many systems like WebSign[1], Vsigns[2] and eSign[3] are using Web3D standards to generate 3D signing avatars, 3D signed sentences are becoming common. Hidden Markov Models is the most used method to recognize sign language from video-based scenes, but in our case, since we are dealing with well formatted 3D scenes based on H-anim and X3D standards, Hidden Markov Models (HMM) is a too costly double stochastic process. We present a novel approach for sign language recognition using Longest Common Subsequence method. Our recognition experiments were based on a 500 signs lexicon and reach 99 % of accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jemni, M., Elghoul, O.: Towards Web-Based automatic interpretation of written text to Sign Language. In: ICTA 2007, Hammamet, Tunisia, April 2007, pp. 12–14 (2007)
Papadogiorgaki, M., Grammalidis, N., Makris, L., Sarris, N., Strintzis, M.G.: Vsign Project. Communication (September 20, 2002), http://vsign.nl/EN/vsignEN.htm
Ehrhardt, U., Davies, B.: A good introduction to the work of the eSIGN project. eSIGN Deliverable D7-2 (August 2004)
Cuxac, C.: La LSF, les voies de l’iconicité. Ophrys editions, Paris (2000)
Web3D consortium website, http://www.web3d.org
Humanoid Animation Standard Group, Specification for a Standard Humanoid: H-Anim 1.1, http://www.h-anim.org/Specifications/H-Anim1.1/
Brutzman, D., Ardly, L.: X3D Extensible 3D Graphics for Web Authors. Elsevier, Amsterdam (2007)
Vogler, C., Metaxas, D.: Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods. In: IEEE International Conference on Computational Cybernetics and Simulation, Oralando, FL (October 1997)
Starner, T., Weaver, J., Pentland, A.: Real-time American Sign Language Recognition using desk and wearable Computer based Video. J. IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)
Rabiner, L.: A tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. Of the IEEE 77, 257–289 (1989)
Euclidean Space by Martin John Baker, http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToEuler/
Bergroth, L., Hakonen, H., Raita, T.: A Survey of Longest Common Subsequence Algorithms. J. SPIRE, 39–48 (2000)
Torgeson, W.S.: Multidimensional scaling of similarity. J. Psychometrika 379–393 (2006)
Deza, M., Deza, E.: Dictionary of Distances, Elsevier editions, Amsterdam (2006)
Jaballah, K., Jemni, M.: Automatic Sign Language Recognition using X3D/VRML Animated Humanoids. In: CVHI 2009, Wroclaw, Poland (April 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jaballah, K., Jemni, M. (2010). Toward Automatic Sign Language Recognition from Web3D Based Scenes. In: Miesenberger, K., Klaus, J., Zagler, W., Karshmer, A. (eds) Computers Helping People with Special Needs. ICCHP 2010. Lecture Notes in Computer Science, vol 6180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14100-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-14100-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14099-0
Online ISBN: 978-3-642-14100-3
eBook Packages: Computer ScienceComputer Science (R0)